DocumentCode :
1516889
Title :
Principal Visual Word Discovery for Automatic License Plate Detection
Author :
Zhou, Wengang ; Li, Houqiang ; Lu, Yijuan ; Tian, Qi
Author_Institution :
Electrical Engineering and Information Science Department, University of Science and Technology of China, Hefei, China
Volume :
21
Issue :
9
fYear :
2012
Firstpage :
4269
Lastpage :
4279
Abstract :
License plates detection is widely considered a solved problem, with many systems already in operation. However, the existing algorithms or systems work well only under some controlled conditions. There are still many challenges for license plate detection in an open environment, such as various observation angles, background clutter, scale changes, multiple plates, uneven illumination, and so on. In this paper, we propose a novel scheme to automatically locate license plates by principal visual word (PVW), discovery and local feature matching. Observing that characters in different license plates are duplicates of each other, we bring in the idea of using the bag-of-words (BoW) model popularly applied in partial-duplicate image search. Unlike the classic BoW model, for each plate character, we automatically discover the PVW characterized with geometric context. Given a new image, the license plates are extracted by matching local features with PVW. Besides license plate detection, our approach can also be extended to the detection of logos and trademarks. Due to the invariance virtue of scale-invariant feature transform feature, our method can adaptively deal with various changes in the license plates, such as rotation, scaling, illumination, etc. Promising results of the proposed approach are demonstrated with an experimental study in license plate detection.
Keywords :
Feature extraction; Image color analysis; Image edge detection; Licenses; Lighting; Training; Visualization; Clustering; geometric context; object detection; principal visual word (PVW); Automobiles; Cluster Analysis; Databases, Factual; Image Processing, Computer-Assisted; Licensure; Pattern Recognition, Automated;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/TIP.2012.2199506
Filename :
6200342
Link To Document :
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